Meteomatics AI Method Enhances Solar and Wind Power Forecasts, Saving Operators and Traders Millions Each Year

Meteomatics has developed a new approach that applies advanced machine learning to power forecasts, enhancing accuracy by 13% for solar and up to 50% for wind. For operators and traders, that means annual savings ranging from tens of thousands to several million dollars, making renewable forecasts more reliable and financially valuable.

Renewable Forecasting Grounded in Real Performance

By combining physics-based weather models with machine learning trained on real power output data, Meteomatics delivers renewable energy forecasts that capture the full complexity of real-world solar and wind systems. Unlike conventional models, which often assume “ideal” operating conditions, this hybrid approach reflects how these systems actually perform.

The result is a new class of forecasts that:

• Reduce financial risk for traders by narrowing uncertainty bands
• Enable grid operators to anticipate fluctuations with greater confidence
• Help asset managers optimize performance and maintenance scheduling

Our models are trained - when data is available - with two years of customer-specific solar farm output, ensuring forecasts are tailored to the realities of each site.

Two levels of solar forecasts are available:

• Farm-level: designed for operators managing the day-to-day performance of solar assets.
• Regional-level: designed for energy traders and grid operators balancing supply and demand.

Traditional physical models translate solar radiation into power output through direct mathematical formulas. But the real world rarely behaves so neatly. AI bridges this gap by capturing complexities that physics alone cannot explain, especially in the presence of:

• Shadows from terrain or vegetation
• Snow cover impacts
• Panel mounting imprecision
• Self-consumption or incomplete grid delivery
• Manual operator interventions

By combining atmospheric and astronomical parameters, including solar radiation, cloud cover and sun position, AI learns subtle patterns such as how shading affects output when the sun is low.

Example: Solar Park in Colombia

In the example below, based on two solar farms from a customer in Colombia, the machine learning model consistently outperformed the purely physical model, delivering 13% lower errors on average month after month. For a typical 100 MW solar park, a 13% improvement in forecast accuracy translates into around $90,000 per year in avoided imbalance costs, money that would otherwise be lost to market penalties. At portfolio scale, the gains quickly reach into the millions.

Wind forecasting presents its own challenges, and AI delivers substantial improvements. By training on past output data, AI models can capture effects that are extremely difficult to reproduce with physical methods, such as:

• Wake effects (wind shadow caused by upstream turbines)
• Ageing effects (reduced performance as turbines wear down)
• Manual interventions (switching modes, altering blade pitch, or capping output)

At present, AI-enhanced forecasts are available at the regional level, supporting traders and grid operators in markets like ERCOT where large-scale accuracy has the greatest impact. Site-specific AI-powered wind farm forecasts are not yet standard but will be available soon, with development already underway. Keep in touch with your customer success manager to stay informed.

Example: ERCOT Market in the US

Meteomatics’ AI-enhanced forecasts achieved up to 50% error reduction in ERCOT, one of the world’s largest and most volatile electricity markets, particularly during spring and summer when price swings are most pronounced. In ERCOT, even a 1% forecast error on a 500 MW wind portfolio can mean over $1 million annually in imbalance and hedging costs. Cutting errors by 50% therefore represents multi-million-dollar savings, providing traders with sharper risk management, operators with greater revenue stability, and grid managers with more reliable integration of renewable supply.